A sequential perspective on searching for static targets

We present a sequential approach to detect static targets with imperfect sensors, which range from tower-mounted cameras to satellites. The scenario is operationally relevant to many military, homeland security, search and rescue, environmental engineering, counter-narcotics, and law enforcement applications. The idea is to stop the search as soon as there is enough probabilistic evidence about the targets’ locations, given an operator-prescribed error tolerance, knowledge of the sensors’ parameters, and a sequence of detection signals from the sensors. By stopping the search as soon as possible, we promote efficiency by freeing up sensors and operators to perform other tasks. The model we develop has the added benefits of decreasing operator workload and providing negative information as a search progresses.     

Stochastic dominance and mean–variance measures of profit and loss for business planning and investment

In this paper, we first extend the stochastic dominance (SD) theory by introducing the first three orders of both ascending SD (ASD) and descending SD (DSD) to decisions in business planning and investment to risk-averse and risk-loving decision makers so that they can compare both return and loss. We provide investors with more tools for empirical analysis, with which they can identify the first-order ASD and DSD prospects and discern arbitrage opportunities that could increase his/her utility as well as wealth and set up a zero dollar portfolio to make huge profit. Our tools also enable investors and business planners to identify the third order ASD and DSD prospects and make better choices.     

Using phase-type models to cost stroke patient care across health,social and community services

Stroke disease places a heavy burden on society, incurring long periods of time in hospital and community care, and associated costs. Also stroke is a highly complex disease with diverse outcomes and multiple strategies for therapy and care. Previously a modeling framework has been developed which clusters patients into classes with respect to their length of stay (LOS) in hospital. Phase-type models were then used to describe patient flows for each cluster. Also multiple outcomes, such as discharge to normal residence, nursing home, or death can be permitted. We here add costs to this model and obtain the Moment Generating Function for the total cost of a system consisting of multiple transient phase-type classes with multiple absorbing states. This system represents different classes of patients in different hospital and community services states. Based on stroke patients’ data from the Belfast City Hospital, various scenarios are explored with a focus on comparing the cost of thrombolysis treatment under different regimes. The overall modeling framework characterizes the behavior of stroke patient populations, with a focus on integrated system-wide costing and planning, encompassing hospital and community services. Within this general framework we have developed models which take account of patient heterogeneity and multiple care options. Such complex strategies depend crucially on developing a deep engagement with the health care professionals and underpinning the models with detailed patient-specific data.     

A multicriteria approach to sustainable energy supply for the rural poor

Despite significant progress in energy technology, about two billion people worldwide, particularly the poor in rural areas of developing countries, have no access to electricity. Decision-making concerning the most appropriate energy technology for supplying these areas has been difficult; existing energy decision-support tools have been useful but are mostly incomplete. Trade-offs, as well as impacts that can be positive or negative, may emerge as a result of implementing modern forms of energy. These can affect both community’s livelihoods as well as the confidence of decision-makers in relation to alternative technologies. The paper discusses a newly designed multicriteria approach and its novel robustness analysis for selecting energy generation systems for the improvement of livelihoods in rural areas. The proposed methodology builds upon a sustainable rural livelihoods framework to address multiple interactions and calculate trade-offs aimed at boosting decision-makers’ confidence in the selected technologies. The methodology is tested via a case study in Colombia.     

Agricultural planning of annual plants under demand,maturation, harvest,and yield risk

In this study we present a planning methodology for a firm whose objective is to match the random supply of annual premium fruits and vegetables from a number of contracted farms and the random demand from the retailers during the planning period. The supply uncertainty is due to the uncertainty of the maturation time, harvest time, and yield. The demand uncertainty is the uncertainty of weekly demand from the retailers. We provide a planning methodology to determine the farm areas and the seeding times for annual plants that survive for only one growing season in such a way that the expected total profit is maximized. Both the single period and the multi period cases are analyzed depending on the type of the plant. The performance of the solution methodology is evaluated by using numerical experiments. These experiments show that the proposed methodology matches random supply and random demand in a very effective way and improves the expected profit substantially compared to the planning approaches where the uncertainties are not taken into consideration.     

Operations research in the space industry

Operations research techniques have been used in the space industry since its infancy, and various competing methods and codes, with widely varying characteristics, have been used over time. This survey is intended to give an overview of current application cases of different operations research techniques and methodologies in the domain of space engineering and space science.     

A flexible multicriteria decision-making methodology to support the strategic management of Science,Technology and Innovation research funding programs

Research funding programs are a policy instrument utilized by governments to influence the innovation process. They are usually elaborated, launched and managed by research funding agencies. In order to select the most adequate research projects, agencies often rely on the peer review process.This paper introduces a methodology to support funding decisions based on the peer review process. The methodology involves the use of a multicriteria decision model to support the assessment, evaluation, prioritization and selection of applications, under a multi-step decision-making process, which fits into a strategic management cycle within the agency. The Multiattribute Value Theory, being considered under a Value Focused Thinking approach, provides a basis for the construction of the multicriteria decision model. The good practices in peer review and also a logical framework for program management are considered by the methodology.A pilot study, presented in the paper, involved a retrospective implementation of a peer review process in the context of a program launched by the Ministry for Science, Technology, Innovations and Communications and the National Council of Technological and Scientific Development, in Brazil. The methodology allowed a clear distinction of roles. The agency staff in the role of decision-makers was responsible for making value judgments on behalf of the agency. The experts, in the role of committee members and ad hoc reviewers, contributed with their expertise by providing objective assessments. Such assessments served as a basis for evaluating the applications, characterizing the possible portfolios, and can be considered as data in future program evaluation studies.     

A look at the past and present of optimization – An editorial

This special issue of the European Journal of Operational Research is devoted to the EURO XXIV Conference, that was held at the facilities of the University of Lisbon (Portugal) from July 11 to July 14, 2010. With over 700 sessions for a total of approximately 2350 presentations, and with 2700 participants (delegates and accompanying persons) coming from 69 countries, this was the largest EURO conference ever.     

On the decisiveness of a game in a tournament

In sport tournaments in which teams are matched two at a time, it is useful for a variety of reasons to be able to quantify how important a particular game is. The need for such quantitative information has been addressed in the literature by several more or less simple measures of game importance. In this paper, we point out some of the drawbacks of those measures and we propose a different approach, which rather targets how decisive a game is with respect to the final victory. We give a definition of this idea of game decisiveness in terms of the uncertainty about the eventual winner prevailing in the tournament at the time of the game. As this uncertainty is strongly related to the notion of entropy of a probability distribution, our decisiveness measure is based on entropy-related concepts. We study the suggested decisiveness measure on two real tournaments, the 1988 NBA Championship Series and the UEFA 2012 European Football Championship (Euro 2012), and we show how well it agrees with what intuition suggests. Finally, we also use our decisiveness measure to objectively analyse the recent UEFA decision to expand the European Football Championship from 16 to 24 nations in the future, in terms of the overall attractiveness of the competition.     

Energy-aware workload management models for operation cost reduction in data centers

In the last century, the costs of powering datacenters have increased so quickly, that datacenter power bills now dwarf the IT hardware bills. Many large infrastructure programs have been developed in the past few years to reduce the energy consumption of datacenters, especially with respect to cooling requirements. Although these methods are effective in lowering the operation costs they do require large upfront investments. It is therefore not surprising that some datacenters have been unable to utilize the above means and as a result are still struggling with high energy bills. In this work we present a cheap addition to or an alternative to such investments as we propose the use of intelligent, energy efficient, system allocation mechanisms in place of current packaged system schedulers available in modern hardware infrastructure cutting server power costs by 40%. We pursue both the quest for (1) understanding the energy costs generated in operation as well has how to utilize this information to (2) allocate computing tasks efficiently in a cost minimizing optimization approach. We were able to underline the energy savings potential of our models compared to current state-of-the-art schedulers. However, since this allocation problem is complex (NP-hard) we investigated various model approximations in a trade-off between computational complexity and allocative efficiency. As a part of this investigation, we evaluate how changes in system configurations impact the goodness of our results in a full factorial parametric evaluation.     

Modeling methods and a branch and cut algorithm for pharmaceutical clinical trial planning using stochastic programming

We discuss methods for the solution of a multi-stage stochastic programming formulation for the resource-constrained scheduling of clinical trials in the pharmaceutical research and development pipeline. First, we present a number of theoretical properties to reduce the size and improve the tightness of the formulation, focusing primarily on non-anticipativity constraints. Second, we develop a novel branch and cut algorithm where necessary non-anticipativity constraints that are unlikely to be active are removed from the initial formulation and only added if they are violated within the search tree. We improve the performance of our algorithm by combining different node selection strategies and exploring different approaches to constraint violation checking.     

An empirical method for assessing the research relevance gap

There has been much debate on the relevance to firms of the academic research produced by business schools. However, what has not received as much attention is how the relevance of the research to businesses should be measured in a systematic and empirical way. We develop a systematic method to test for the relevance of academic research to businesses. Our method models as a vector autoregressive process the interests of the academic and practitioner communities in some new topic, as expressed by the number of articles published in the academic and the practitioner literature on that topic per calendar quarter, and then studies Granger causality between the academic and practitioner interest processes. This method can be used by academics to empirically demonstrate the impact of their intellectual contributions on practitioners and thence on the business world. We employ our approach to two relatively new and important topics, Real Options and Economic Value Added.     

Decision making with hybrid influence diagrams using mixtures of truncated exponentials

Mixtures of truncated exponentials (MTE) potentials are an alternative to discretization for representing continuous chance variables in influence diagrams. Also, MTE potentials can be used to approximate utility functions. This paper introduces MTE influence diagrams, which can represent decision problems without restrictions on the relationships between continuous and discrete chance variables, without limitations on the distributions of continuous chance variables, and without limitations on the nature of the utility functions. In MTE influence diagrams, all probability distributions and the joint utility function (or its multiplicative factors) are represented by MTE potentials and decision nodes are assumed to have discrete state spaces. MTE influence diagrams are solved by variable elimination using a fusion algorithm.     

Discrete time modeling of mean-reverting stochastic processes for real option valuation

In this paper the recombining binomial lattice approach for modeling real options and valuing managerial flexibility is generalized to address a common issue in many practical applications, underlying stochastic processes that are mean-reverting. Binomial lattices were first introduced to approximate stochastic processes for valuation of financial options, and they provide a convenient framework for numerical analysis. Unfortunately, the standard approach to constructing binomial lattices can result in invalid probabilities of up and down moves in the lattice when a mean-reverting stochastic process is to be approximated. There have been several alternative methods introduced for modeling mean-reverting processes, including simulation-based approaches and trinomial trees, however they unfortunately complicate the numerical analysis of valuation problems. The approach developed in this paper utilizes a more general binomial approximation methodology from the existing literature to model simple homoskedastic mean-reverting stochastic processes as recombining lattices. This approach is then extended to model dual correlated one-factor mean-reverting processes. These models facilitate the evaluation of options with early-exercise characteristics, as well as multiple concurrent options.     

Quantitative models for sustainable supply chain management: Developments and directions

Sustainability, the consideration of environmental factors and social aspects, in supply chain management (SCM) has become a highly relevant topic for researchers and practitioners. The application of operations research methods and related models, i.e. formal modeling, for closed-loop SCM and reverse logistics has been effectively reviewed in previously published research. This situation is in contrast to the understanding and review of mathematical models that focus on environmental or social factors in forward supply chains (SC), which has seen less investigation. To evaluate developments and directions of this research area, this paper provides a content analysis of 134 carefully identified papers on quantitative, formal models that address sustainability aspects in the forward SC. It was found that a preponderance of the publications and models appeared in a limited set of six journals, and most were analytically based with a focus on multiple criteria decision making. The tools most often used comprise the analytical hierarchy process or its close relative, the analytical network process, as well as life cycle analysis. Conclusions are drawn showing that numerous possibilities and insights can be gained from expanding the types of tools and factors considered in formal modeling efforts.     

Systematic analysis of framing bias in missile defense: Implications toward visualization design

This paper examines the effects of framing on decision making in a homeland missile defense context across three tasks of varying complexity. Mathematically, each task was modeled as abstractions from a common resource allocation task. Logically, therefore, the effects of framing on human subjects should have been consistent across all the tasks. In the first experiment, a simple lottery was used to determine risk postures in a single-attribute case of missile defense. Results showed that, consistent with Prospect Theory, positive framing promotes risk-averse behavior whereas negative framing promotes risk-seeking behavior. In the second experiment, we used the Analytic Hierarchy Process to determine subject rankings in a multi-attribute case of missile defense. Results suggest that subjects’ performances under positive framing were significantly better than performances under negative framing. In the third experiment, we used a human-in-the-loop simulation to elicit human decisions in a missile defense resource allocation task. In comparison to the other experiments, the framing effect in the third experiment was diminished. We submit that decision biases detected in a simple choice task cannot be assumed to carry over to tasks of greater complexity even if the underlying mathematical formulation for all the tasks is the same. Moreover, we submit that the design of the graphical interface has a greater influence on human judgment bias than framing in tasks of higher complexity.     

Modelling impacts of cropping systems: Demands and solutions for DEX methodology

Decision modelling of diverse groups of problems makes different requirements to the modelling methodologies and software. We present an actual decision problem and the required characteristics of corresponding decision models. The problem is from agronomy and addresses the ecological and economic impacts of cropping systems, with the focus on the differences between cropping systems with conventional crops and the ones with genetically modified crops. We describe the extensions of an existing DEX qualitative multi-attribute modelling methodology, which were made to cope with the challenges of the problem. The extensions address general hierarchical structures, probabilistic utility functions and numerical values of basic attributes. A new, freely available software tool called proDEX was implemented to support the extended methodology. In this paper we describe the problem of cropping system assessment, propose methodological extensions to DEX, and present the implementation of proDEX.     

Cost-efficiency analysis of weapon system portfolios

Decisions about the acquisition and maintenance of military equipment serve to build long-term capabilities in preparation of military conflicts. Typically, these decisions involve large investments which need to be supported by adequate cost-efficiency analyses. Yet the cost-efficiency analysis of weapon systems involves several challenges: for example, it is necessary to account for the possible interactions among different weapon systems; the relevance of several impact criteria; and the variety of combat situations in which these systems may be used. In this paper, we develop a portfolio methodology where these challenges are addressed by evaluating the cost-efficiencies of entire portfolios consisting of individual weapon systems. Our methodology accounts for possible interactions among systems by synthesizing impact assessment results that are either generated by combat simulation models or elicited from experts. It also admits incomplete preference information about the relative importance of different impact criteria. This methodology guides decision making by identifying which combinations of weapon systems are efficient with respect to multiple evaluation criteria in different combat situations at different cost levels. It can also be extended to settings where multiple combat situations are addressed simultaneously. The methodology is generic and can therefore be applied also in civilian settings when portfolios of activities (such as mitigation of harmful environmental emissions) may exhibit interactions.     

International trade and biological invasions: A queuing theoretic analysis of the prevention problem

We propose and develop a new framework for studying the problem of preventing biological invasions caused by ships transporting internationally traded goods between countries and continents. Specifically, we apply the methods of queuing theory to analyze the problem of preventing a biological invasion from a long run perspective. First, we characterize two simple regulatory regimes as two different kinds of queues. Second, we show how to pose a publically owned port manager’s decision problem as an optimization problem using queuing theoretic techniques. Third, we compare and contrast the optimality conditions emanating from our analysis of the M/M/I/U and the M/M/I/I inspection regimes. We conclude by discussing possible extensions to our basic models.